>>Live from Copenhagen, Denmark. It's the Q covering Nutanix dot. Next 2019 brought to you by Nutanix. >>Welcome back everyone to the cubes live coverage of dot. Next here in Copenhagen. I'm your host, Rebecca Knight along with my cohost Stu Miniman. We are joined by Sammy Zog LaMi. He is the SVP sales Europe, Nutanix and Sylvan CU. He is the senior director systems engineer for EMEA at Newtanics. Thank you so much for coming on the cube for you for returning. And this is your first time. >>First time. Absolutely. >>Well I want to, I want to start with you. You were on the main stage this morning and you were describing being one of the first few employees in France, working out of hotel lobbies, keeping all the promotional materials in your house and people not even knowing how to pronounce Nutanix. Now here you are for you six years later. Describe, describe a little bit what it, what, what this journey has been like for you. Being at Nutanix >>for this journey. Um, you know, is a, is a successful journey obviously, uh, where we started from scratch in Eva, uh, where we built a lot of relationship with the channel. We started to have our first stories with customers and, uh, you know, the only thing we could not, uh, you know, focus was the speed of growth. And I, if you told me six years ago that we would be four and a half thousand, you know, in this conference, I wouldn't have believed it. And I think the, you know, overall journey is a, you know, an accelerated journey of development and that we have, >>yeah, Sam, Sammy, prednisone side, a little bit about, uh, you know, we sometimes call it nation building, but, uh, you know, the channel of course, a very important, uh, you know, talked about some of the, kind of, the challenges in, uh, some of the successes as to what, what has made Nutanix so successful, uh, in, in your time. Yeah. I think, uh, >>you know, the technology is for sure a big element in this that is solving business problems. But when you think about it, there's many stories of great technologies that didn't make it or didn't make it big. So I think the openness of this company from day one, uh, to work with partners to work with an ecosystem of Alliance partners. Uh, we were also very open to share how the Nutanix technology is built and is working. So there's a lot of openness around your Hasise works. It's not a black box. Uh, and we integrate with the ecosystem. So for our positioning, which is mainly initially the data center, the large environments we have to integrate into customer environment, we have to integrate with existing technologies and uh, the fact that we are open from day one and we keep that line is helping a lot in the traction. >>I want to get into that strategy in a little bit, but I want to bring you into this conversation to Sylvan and, and just to have you talk a little bit about what you're seeing in the competitive landscaping, what, what are some of the things that Nutanix needs to focus on? Because the competitors are a really edging in. We are focused to deliver >>our vision and continue to build the pieces that are still under construction there right now. And to be back on the question about the partners, the adoption also come first from the partners before their customers. And really working with them on engaging with them was the result of the success was not just signing contract enabled them, but really engaging with them at customer sites. And as soon as they see the reaction of the customer, they can be believe in it. And we scaled very fast because of them. I'm wondering, get both of your comments. Talk about the, uh, the competition for talent. Also, when you talk about Nutanix over 5,000, the channel is very strong. It makes it a little bit tougher, uh, to kind of pull those pieces in. If you're Silicon Valley, Oh, there's this startup, I want to join, things like that, but have to imagine things are a little bit different. And I'm in Mia, >>I would say. Well, competition for talent is definitely here in Emir, especially on the topics that we are tackling in the cloud, the DevOps, big data, et cetera. Um, now, you know, we are not attractive brand, uh, you know, there's a demonstrated pass of development for our employees. So I think on top of being a successful company, we have a lot of proof points of building careers. So people want to join for the fun for the success. We are also to be able to fast career. That's helps now saying that it's still not an easy task. You know, there's a, especially the volume of recruitment we are doing, uh, so we have organized ourselves very well, uh, to onboard people, enable people and maybe be in a position to hire people that don't have all the skills but have the right DNA and then we can, you know, always teach the skills. That's the way we are. >>and on a technical side, uh, all the user's previous it vendor let's say, was looking for specialists of complexity. You know, what is the behind the scene and we are in different situation, meaning that we can start small first and we talk about the project of the customer. And until this project works, we cannot move forward. We cannot obsessive. So our situation is more consultative and being a trusted advisor of what they tried to achieve and not anymore on what we tried to build our own our side. >>That's a very important point. The mindset of successful employees are the ones that are focused on the outcomes. You know, they're not here to sell a product, they focus on project and the outcome of customers. >>So how do you find that person when you are, when you're interviewing your pool of applicants? I mean that, that is, that is such an important part of the culture here, this people first attitude and really being all hands on deck if a customer has an issue. So how do you, how do you know when you're interviewing someone that, that, that they have got their, the right DNA to be here? >>Well, first we knew before they, during the interview, because we are well connected on the market and we have sources of information about how they operate on day to day. Now, of course, of hiring so many people over the years helps. And there's a lot of small details that, you know, we can notice, uh, in, uh, in our recruitment process. I think we've gotten very professional in the way we recruit. We still have a lot of refills as well from employees, which helps in terms of, uh, you know, making sure we hiring the right DNA, but we want to diversify. We don't want people coming from the same background. We're doing a pretty good job on diversity, on every topic, you know, gender, ethnicity, background, uh, this is a, you know, pretty good success. Alright, so >>semi you, you've got a new role. So it gives us a little bit of insight as to your vision. What should we should expect to see as a strategy for Nutanix and EMEA? >>I would say first, uh, you know, three months on the job and I have no intent to break anything that works. Uh, I think there's a successful recipe in anemia, which is a legacy of Chris Keller Ross. Uh, lots of good methodologies, verse of good principles of working, no intention to change that and maybe the phase after that for MEA, but for the whole company is to focus on Australia. And we see that, you know, our technology is well suited for mission critical environment is well suited for strategic projects for customers. And maybe we should become the default, uh, you know, uh, vendor that you think about when you go for mission critical projects and you know, trust formation. Uh, I think today we do a very broad set of projects with customers. Um, tomorrow I would like customers to think first about Nutanix when they think about something that is critical to their business. >>And in the same way for partners, uh, if we can move from being a vendor with high grows, great margin to a vendor that is helping them transform, you know, their business model or the way they attack different segments, you know, then we will have achieved a good phase two. What do you see as the biggest challenges facing you right now? Well, the biggest challenge is inside clearly is growth. We see that in every area, every time we grow fast, then suddenly you need to change organization processes, your principle of working and you, you need to reassess yourself and your way of doing things. Even at pesonal level. Uh, that's the biggest challenge. I think we, if we are not constantly paranoid about re re assessing that uh, growth can break a lot of quality, uh, in the relationship we have with customers but also in our velocity. >>Oh, I wonder if you could bring us inside the customers a little bit. What are some of the key roles that you find in, you know, where does Nutanix has the best engagement with and you know, strategically where would Nutanix may be a change over time as to where they're, where they're engaging with a customer. >>So now there is no more question about the fact that part of the, it will be in the cloud part will be internally, some people will go more one side or the other side because Nutanix both technology >>on both sides, we can take care of old school application and be sure that can still run in the cloud. And on this society, if you develop an application totally distributed and so on, meaning a cloud native, we can run it on a Nutanix and all the platform looks like the pubic cloud for this application. So we are the unique situation where we can, we don't need to be in the cloud or outside of the cloud, meaning that we can give a strategy with the customers or what it can do. What is the good point, what is the most difficult to achieve on both sides. And also we provide a way to package application to deploy everywhere. We have all these governance tools on top of it because we know the new way of consuming the cloud is more open bar, which you need some way of controlling the situation and we are really trusted advisor on their strategy to define what will be their it in two, three or four years. >>Okay. So sounds like not just the infrastructure owner but talking to the application owner or some of the C suite that might make some of those broader strategic decisions. >>Yeah. Yeah, exactly. Uh, the platform works, meaning that there is no more cushion on that at scale. You get all the benefits that you can see on the, on the public cloud. Now it's more the way you consume it, you organize the consumption and also you've have those, the same of urine Mount whatever is the application, uh, to, to find the, to have the best place for this application. >>What would you say your, your, your here as you said, uh, at in Copenhagen, thousands of European customers all here under one roof. What are you getting out of this? What kind of conversations are you hearing? What's most surprising to you? Just to, I mean we're, I know we're only in the beginning of day one, but what, what do you, what are you hearing right now? >>Well, we talked to a few customers already and what's a very common pattern? Most of the customers I took so far, they really accelerating on becoming a service organization. So enterprise companies, they really want to organize themselves to be cloud ops. And even though we were talking about automation before, now they really are doing it and they are actually focusing on changing the skills of their teams, their organizations and of course the technology afterwards. >>Yeah. Uh, any, any particular is on automation. Cause I think back, we've been talking about automation my entire career. I agree with you today. It is a, you know, more substantial conversation on automation. Are there any particular as either in Newtanics portfolio where some of the kind of partner tooling out there that are kicking things along? >>So, uh, we talk about automation since a long time, but most of the time that was, you have an orchestrator, it's like a Swiss knife and you can orchestrate what you want, but at the end of the day, nothing was done. We believe that the platform must be automated by design, right? And everything need to be by design. So it's a, it's the difference between the, between the previous way of thinking, automation and now where the platform is totally it. >>I believe Leber GF said autonomous is what >>we were looking for. Yes. You got to the point. If it's not autonomous, why? Why bother? Yeah. Or we had examples of customers who launched private cloud projects and they had like 8,000 Mondays to build the orchestration of the private cloud. And honestly, if you don't have a a hundred thousand VMs to run, it makes no sense. So the fact that no, it's built in and it's not a project to have automation, you know, that makes sense economically as well. Great. Well semi and see you. Thank you so much for coming on the cube. It's a pleasure having you later, Rebecca. Thanks a lot. Thank you. I'm Rebecca Knight for Stu Mittleman. Stay tuned for more of the cubes live coverage of.next.

>> from our studios in the heart of Silicon Valley, Palo Alto, California It is a cute conversation >> lover on Welcome to this cube conversation here in Palo Alto, California. The Cube Studio. I'm John for the co host of the Cube Weird Sylvan shop. Who's the head of solution Architecture and open systems securing Esti win of right of other cloud to point out like capabilities. Very successful. 20 plus years. Operation Civil was the one of the first folks are coming over to the US to expand their operation from Europe into New York. Now here in Silicon Valley. Welcome to the Cube conversation. Thank you. So instituting trivia. You were part of the original team of three to move to the U. S. From Switzerland. You guys had phenomenal success in Europe. You've come to the U. S. Having phenomenal success in the US Now you moving west out here to California on that team, you're opening things up at the market. >> It's been a chance, Mikey. Things can presented themselves step by step, and I jumped on the trains and it's been a good right. >> Awesome. You guys have had great success. We interviewed your CEO a variety of your top people. One of the things that's interesting story is that you guys have been around for a long time. Been there, done that, riding this next next wave of digital transformation. What we call a cloud two point. Oh, but really is about enterprise. Full cloud scale, securing it. You have a lot of organic growth with customers, great word of mouth. So that's not a lot of big marketing budgets, riel. Real success there. You guys now are in the US doing the same thing here. What's been the key to success for open systems wide such good customers? Why the success formula is it you guys are on the right wave. What is it? The product? All the above. What's the What's the secret formula? >> So multiple things I say. And we started as a privately owned company like broad banks to, um, to the Internet email into one back in the nineties. And, um, yeah, we started to grow organically, as he said were by mouth, and Indiana is we put heavy focus on operations, so we wanted to make our customers happy and successful, and, um, yeah, that's how we got there like it was slow organic growth. But we always kind of kept the core and we tried to be unconventional, tried to do things differently than others do. And that's what brought us to where we are today and now capabilities Being here in the Valley, um, opens up a lot of more doors. >> It's got a nice office and we would see I saw the video so props for that. Congratulations. But the real to me, the meat on the bone and story is, is that and I've been really ranting on this whole SD win is changing. SD Win used to be around for a long, long time. It's been known industries known market. It's got a total addressable market, but really, what has really talks to is the the cloud. The cloud is a wide area network. Why do we never used to be locked down? He had the old way permitted based security. Now everything is a wide area. That multi cloud in hybrid club. This is essentially networking. It's a networking paradigms. It's not lately rocket science technically, but the cloud 2.0 shift is about, you know, data. It's about applications, different architectures you have everything kind of coming together, which creates a security problem, an opportunity for new people to come in. That's what you guys? One of them. This is the big wave. What? It explain the new s t win with, you know, the old way and the new way. What is the what? What should people know about the new S D win marketplace? >> Yeah. So let me start. Where do Owen has come from and how digital transformation has impacted that. So typically corporate wider networks were centered around the Clear Data Center where all applications were hosted, storage and everything and all traffic was back holding to the data center. Typically, one single provider that Broady, Mpls links on dhe. It was all good. You had a central location where you could manage it. You had always ability security stack was there. So you had full control. Now new requirements from natural transformation broad as users are on the road, they're on their phones ipads on the in, restaurants in ah, hotels, Starbucks. Wherever we have applications moved to the cloud. So their access directly You wanna have or be as close as possible Unify Communications. I OT It's all things deposed. Different requirements now in the network and the traditional architecture didn't were wasn't able to respond to that. It's just that the links they were filled up. You couldn't invest enough thio blow up your Nampula slings to handle the band with You lost visibility because users were under road. You lost control, and that's where new architectures had to be found. That's where Ston step them and say, Hey, look now we're not centered around the headquarter anymore were sent around where the applications are, your scent around, where the data is, and we need to find means to connected a data as quickly as possible. And so you can use the Internet. Internet has become a commodity. It's become more performance more stable, so we can leverage that we can route traffic according to our policies. We can include the cloud, and that's where Ston actually benefits from the clown. As much as the club benefits from SD went because they go hand in hand and that's also what we really drive to say, Hey, look, now the cloud can be directly brought into your network, no matter where, where data and where applications. >> Yeah, and this is the thing. You know, Although you've been critical of S t when I still see it as the path of the future because it's networking. And the end of the day networking is networking. You moving packets from point A to point B and you're moving somebody story you moving from point A to store the point C. It's hard. And you brought this up about Mpls. It's hard to, like rip and replace You can't just do a wholesale change on the network has the networks are running businesses. So this is where the trick is, in my opinion. So I want to get your thoughts on how companies were dealing with this because, I mean, if you want to move, change something in the network, it takes a huge task. How did you guys discover this new opportunity? How did you implement it? What was the and how should customers think about not disrupting their operations at the same time bringing in the new capabilities of this SD win two point? Oh, >> yeah, that's it's a perfect sweet spot, because in the end is, um, nobody starts at a green field. If you could start with a green field. It's easy. You just take on the new technology and you're happy. But, um, customers that we look up large enterprises, they have a brownfield. They haven't existing that work. They have business critical applications running 24 7 And if you look at what options large enterprises have to implement and manage a nasty when is typically three approaches, they either do it themselves, meaning they need a major investment in on boarding people having the talent validating technology and making the project work already. Look at a conventional managers provider. In the end, that is just the same as doing yourself. It's just done by somebody else, and you have the the challenge that those providers typically, um, have a lot of portfolio that they manage. And they do not have enough expertise in Nasty Wen. And so you just end up with the same problems and a lot of service, Janey. So even then you do not get the expertise that you need. >> I think what's interesting about what you guys have done? I want to get your reaction to this is that the manage service piece of it makes it easier to get in without a lot of tinkering with existing infrastructure. Exact. And that's been one of that tail winds for you guys and success wise. Talk about that dynamic of why they managed service is a good approach because you put your toe in the water, so to speak, and you can kind of get involved, get as much as you need to go and go further. Talk about that dynamic and why that's important. >> Yeah, technology Jane is very quickly. So you need people that are able to manage that and open systems as a pure play provider. We build purposely build our platform for us, he went. So we integrated feature sets. We we know how to monitor it, how to configure it, how to manage it. Lifecycle management, technology, risk technology management. All this is purposely purposely built into it, so we strongly believe that to be successful, you need people that are experts in what they do to help you so that you and your I t people can focus in enabling the business. And that's kind of our sweet spot where we don't say we have experts. Our experts operating the network for you as a customer and therefore our experts are your experts. And that's kind of where we believe that a manage service on the right way ends up in Yeah, the best customer. >> And I think the human capital pieces interesting people can level up faster when you when you're not just deploying here. Here's the software load. It is the collaborations important. They're good. They're all right. While you're on this topic, I want to get your thoughts. Since you're an expert, we've been really evaluating this cloud 2.0, for lack of a better description. Cloud 2.0, implying that the cloud 1.0 was Amazon miss on The success of Amazon Web service is really shows Dev Ops in Action Agility The Lean startup Although all that stuff we read reading about for the past 10 plus years great compute storage at scale, amazing use of data like you, said Greenfield. Why not use the cloud? Great. Now all the talk about hybrid cloud even going back to 2013 We were of'em world at that time start 10th year their hybrid cloud was just introduced. Now it's mainstream now multi cloud is around the corner. This teases out cloud 2.0, Enterprise Cloud Enterprise Scale Enterprise Security Cloud Security monitoring 2.0, is observe ability. Got Cooper All these new things air coming on. This is the new clout to point out what is your definition of cloud two point? Oh, if you had to describe it to a customer or a friend, >> it is really ah, some of hybrid cloud or multi cloud, as you want to name it, because in the end, probably nobody can say I just select one cloud, and that's going to make me successful because in the end, cloud is it's not everywhere, as we kind of used to believe in the beginning, but in the end, it's somebody else's computer in a somebody else's data center. So the cloud is you selectively pick the location where you want to for your cloud instances and asked if Cloud Service providers opened up more locations that are closer to your users in the or data you actually can leverage more possibilities. So what we see emerging now is that while for a long time everything has moved to the cloud, the cloud is again coming back to us at the sietch. So a lot of compute stuff is done close to where data is generated. Um, it's where the users are. I mean, Data's generated with with us. Yeah, phones and touch and feel and vision and everything. So we can leverage these technologies to really compute closer to the data. But everything controlled out of central cloud instances. >> So this brings up a good point. You essentially kind of agreeing with cloud one detto being moved to the cloud. But now you mentioned something that's really interesting around cloud to point out, which is moving having cloud, certainly public clouds. Great. But now moving technology to the edge edge being a data center edge being, you know, industrial I ot other things wind farms, whatever users running around remotely you mentioned. So the edges now becomes a critical component of this cloud. Two point. Oh, okay. So I gotta ask the question, How does the networking and what's the complexity? And I'm just imagining massive complexity from this. What are some of the complexities and challenges and opportunities will arise out of this new dynamic of club two point. Oh, >> So the traditional approaches does just don't work anymore. So we need new ways to not only on the networking side, but obviously also the security side. So we need to make sure that not on Lee the network follows in the footsteps of the business of what it needs. But actually, the network can drive business innovation and that the network is ready to handle those new leaps and technologies. And that's what we see is kind of being able to tightly integrate whatever pops up, being able to quickly connect to a sass provider, quickly integrate a new cloud location into your network and have the strong security posture there. Directly integrated is what you need because if you always have to think about weight, if I add this, it's gonna break something else, and I have to. To change is here. Then you lose all the speed that your business needs. >> I mean, the ripple effect of it's like throwing a stone in the lake and seeing the ripple effect with cloud to point. You mentioned a few of them. Network and Security won't get to that in a second, but doesn't change every aspect of computing categories. Backup monitoring. I mean all the sectors that were traditional siloed on premise that moves with the cloud are now being disrupted again for the third time. Yeah, you agree with that? >> It's true. And I mean your club 0.1 point. Oh, you say a lot of things will be seen his lift and shift and that still works like there is a lot of work loads where it's not worth it to re factor everything. But then, for your core applications, the business where the business makes money, you want a leverage, the latest instead of technologies to really drive, drive your business there. >> I got to get your take on this because you're the head of architecture solutions at Open Systems. Um, is a marketing tagline that I saw that you guys promote, which I live. I want to get your thoughts on. It says, Stop treating your network like a network little marketing. I love it, but it's kind of like stop trying your network like a network implying that the networks changing may be inadequate. Antiquated needs to modernize. I'm kind of feeling the vibe there on that. What do you mean by that? Slow Stop treating your network like a network. What's what's the purpose >> behind that? But yeah, in the end, it to be a little flaw provoking. But I mean, even est even in its pure forms, where you have a softer controller that steers your traffic along different path. Already. For me, as an engineer, I'm gonna lose my mind because I want to know where routing is going. I want deterministic. Lee defined my policy, so I always have things under control. But now it's a softer agent that takes care. Furred takes care of it for me so that already I lose control in favor off. Yeah, more capabilities. And I think that's cloud just kind of accelerate. >> So you guys really put security kind of in between the network and application? Is that the way you're thinking about it? It used to be Network was at the bottom. You built the application, had security. Now you're thinking differently. Explain that the the architectural thinking around this because this is a modern approach you guys were taking, and I want to get this on the record. Applications have serving users and machines network delivers packets, and then you're saying security's wrapping up between them explain. >> So when we go back again to the traditional model Central Data Center, you had a security stack full rack of appliances that the care of your security was easy to manage. Now, if you wanna go ask you when connect every brand side to the Internet, you cannot replicate such an infrastructure to every branch. Location just doesn't skill. So what do you do? Why do you say I cannot benefit of this where I use new methods? And that's where we say we integrate security directly into our networking stack. So to be able to not rely on the service training but have everything compiled into one platform and be able to leverage that data is passing through our network. You've eyes. But then why not apply the same security functions that we used to do in our headquarter directly at the edge and therefore every branch benefits of the same security posture that I typically were traditionally only had in my data center? >> You guys so but also weighing as a strategic infrastructure critical infrastructure opponent. I would agree with that. That's obvious, but as we get into hybrid cloud and multi cloud infrastructures of service support. Seamless integration is critical. This has become a topic, will certainly be talking about for the rest of the year Of'em world and reinvented other conferences like Marcel that night as well. This is the big challenge for customers. Do I invest in Azure A. W as Google in another cloud? Who knows how many clouds coming be another cloud potentially around the corner? I don't want to fork my development team. I want to do one of the great different code bases. This has become kind of like the challenge. How do you see this playing out? Because again, the applications want to run on the best cloud possible. I'm a big believer in that. I think that the cloud should dictate the AP should dictate which cloud runs. That's why I'm a believer in the single cloud for the workload, not a single cloud for all workloads. So your thoughts, >> I think, from an application point of view. As you say, the application guys have to determine more cloud is best for them, I think from a networking point of view, as a network architect, we need to we can't work against this but enable them and be able to find ways that the network can seamlessly connect to whatever cloud the business wants to use. And there's plenty of opportunity to do that today and to integrate or partner with other providers that actually have partnered with dozens of cloud providers. And as we now can architect, we have solutions to directly bring you as a customer within milliseconds, to each cloud, premise is a huge advantage. It takes a few clicks in a portal. You have a new clouds instance up and running, and now you're connected. And the good thing is, we have different ways to do that. Either. We spin up our virtual instance virtual esti one appliance in cloud environments so we can leverage the Internet to go. They're still all secured, all encrypted, ordering me again. Use different cloud connect interconnections to access the clouds. Depending on the business requirements, >> you guys have been very successful. A lot of comfort from financial service is the U. N. With NGOs, variety of industries. So I want to get your thoughts on this. I've been we've been covering the Department of Defense is joining and Chet I joint and the presentation of defense initiative where the debate was soul single purpose Cloud. Now the reality is and we've covered this on silicon angle that D O D is going multi cloud as an organization because they're gonna have Microsoft Cloud for collaboration and other contracts. They're gonna win $8,000,000,000. So that a Friday cloud opportunities, but for the particular workload for the military, they have unique requirements. Their workload has chosen one cloud. That was the controversy. Want to get your thoughts on this? Should the workloads dictate the cloud? And is that okay? And certainly multi cloud is preferred Narada instances. But is it okay to have a single cloud for a workload? >> Yeah, again, from if the business is okay with that, that's fine from our side of you. We see a lot of lot of business that have global presence, so they're spread across the globe. So for them, it's beneficial to done distribute workloads again across different regions, and it could still be the same provider, but across different regions. And then already, question is How do you now we're out traffic between those workloads? Do we? Do you love right? Your esteem and infrastructure or do you actually use, for example, the backbone that the cloud provider provides you in case of Microsoft? They guarantee you the traffic between regions stay in their backbone. So gifts, asshole, new opportunities to leverage large providers. Backbone. >> And this is an interesting nuance point because multi cloud doesn't have to be. That's workload. Spreading the workload across three different clouds. It's this workload works on saving Amazon. This workload works on Azure. This workload works on another cloud that's multi cloud from a reality standpoint today, so that implies that most every country will be multi cloud for sure. But workloads might have a single cloud for either the routing and the transit security with the data stored. And that's okay, too. >> Yeah, yeah, and keep in mind, Cloud is not only infrastructure or platform is the service. It's also software as a service. So as soon as we have sales forests, work day office 3 65 dropbox or box, then we are multiplied. >> So basically the clouds are fighting it out by the applications that they support and the infrastructure behind. Exactly. All right, well, what's next for you? You're on the road. You guys doing a lot of customer activity. What's the coolest thing that you're seeing in the customer base from open system standpoint that you like to share with the audience? >> Um, so again, it's just cool to see that customers realized that there's plenty of opportunities. And just to see how we go through that evolution with our customers, were they initially or little concerned? But then eventually we see that actually, the network change drives new business project and customers air happy that they launched or collaborate with us. That's what that's what makes me happy and makes me and a continuing down that path >> and securing it is a key. Yeah, he wins in this market Having security? >> Absolutely. Yeah, Sylvia saying mind and not wake up at 2 a.m. Full sweat, because here >> we'll manage. Service is a preferred for my people like to consume and procure product in So congratulations and congressional on your Silicon Valley office looking for chatting more. I'm John for here in the keep studios for cute conversation. Thanks for watching

>> Announcer: Live from Santa Fe, California, it's the Cube covering Big Data Silicon Valley 2017. >> Welcome, back, everyone. We're here live in Silicon Valley in San Jose for Big Data SV in conjunct with stratAHEAD Hadoop part two. Three days of coverage here in Silicon Valley and Big Data. It's our eighth year covering Hadoop and the Hadoop ecosystem. Now expanding beyond just Hadoop into AI, machine learning, IoT, cloud computing with all this compute is really making it happen. I'm John Furrier with my co-host George Gilbert. Our next guest is Arik Pelkey who is the senior director of product marketing at Pentaho that we've covered many times and covered their event at Pentaho world. Thanks for joining us. >> Thank you for having me. >> So, in following you guys I'll see Pentaho was once an independent company bought by Hitachi, but still an independent group within Hitachi. >> That's right, very much so. >> Okay so you guys some news. Let's just jump into the news. You guys announced some of the machine learning. >> Exactly, yeah. So, Arik Pelkey, Pentaho. We are a data integration and analytics software company. You mentioned you've been doing this for eight years. We have been at Big Data for the past eight years as well. In fact, we're one of the first vendors to support Hadoop back in the day, so we've been along for the journey ever since then. What we're announcing today is really exciting. It's a set of machine learning orchestration capabilities, which allows data scientists, data engineers, and data analysts to really streamline their data science processes. Everything from ingesting new data sources through data preparation, feature engineering which is where a lot of data scientists spend their time through tuning their models which can still be programmed in R, in Weka, in Python, and any other kind of data science tool of choice. What we do is we help them deploy those models inside of Pentaho as a step inside of Pentaho, and then we help them update those models as time goes on. So, really what this is doing is it's streamlining. It's making them more productive so that they can focus their time on things like model building rather than data preparation and feature engineering. >> You know, it's interesting. The market is really active right now around machine learning and even just last week at Google Next, which is their cloud event, they had made the acquisition of Kaggle, which is kind of an open data science. You mentioned the three categories: data engineer, data science, data analyst. Almost on a progression, super geek to business facing, and there's different approaches. One of the comments from the CEO of Kaggle on the acquisition when we wrote up at Sylvan Angle was, and I found this fascinating, I want to get your commentary and reaction to is, he says the data science tools are as early as generations ago, meaning that all the advances and open source and tooling and software development is far along, but now data science is still at that early stage and is going to get better. So, what's your reaction to that, because this is really the demand we're seeing is a lot of heavy lifing going on in the data science world, yet there's a lot of runway of more stuff to do. What is that more stuff? >> Right. Yeah, we're seeing the same thing. Last week I was at the Gardener Data and Analytics conference, and that was kind of the take there from one of their lead machine learning analysts was this is still really early days for data science software. So, there's a lot of Apache projects out there. There's a lot of other open source activity going on, but there are very few vendors that bring to the table an integrated kind of full platform approach to the data science workflow, and that's what we're bringing to market today. Let me be clear, we're not trying to replace R, or Python, or MLlib, because those are the tools of the data scientists. They're not going anywhere. They spent eight years in their phD program working with these tools. We're not trying to change that. >> They're fluent with those tools. >> Very much so. They're also spending a lot of time doing feature engineering. Some research reports, say between 70 and 80% of their time. What we bring to the table is a visual drag and drop environment to do feature engineering a much faster, more efficient way than before. So, there's a lot of different kind of desperate siloed applications out there that all do interesting things on their own, but what we're doing is we're trying to bring all of those together. >> And the trends are reduce the time it takes to do stuff and take away some of those tasks that you can use machine learning for. What unique capabilities do you guys have? Talk about that for a minute, just what Pentaho is doing that's unique and added value to those guys. >> So, the big thing is I keep going back to the data preparation part. I mean, that's 80% of time that's still a really big challenge. There's other vendors out there that focus on just the data science kind of workflow, but where we're really unqiue is around being able to accommodate very complex data environments, and being able to onboard data. >> Give me an example of those environments. >> Geospatial data combined with data from your ERP or your CRM system and all kinds of different formats. So, there might be 15 different data formats that need to be blended together and standardized before any of that can really happen. That's the complexity in the data. So, Pentaho, very consistent with everything else that we do outside of machine learning, is all about helping our customers solve those very complex data challenges before doing any kind of machine learning. One example is one customer is called Caterpillar Machine Asset Intelligence. So, their doing predictive maintenance onboard container ships and on ferry's. So, they're taking data from hundreds and hundreds of sensors onboard these ships, combining that kind of operational sensor data together with geospatial data and then they're serving up predictive maintenance alerts if you will, or giving signals when it's time to replace an engine or complace a compressor or something like that. >> Versus waiting for it to break. >> Versus waiting for it to break, exactly. That's one of the real differentiators is that very complex data environment, and then I was starting to move toward the other differentiator which is our end to end platform which allows customers to deliver these analytics in an embedded fashion. So, kind of full circle, being able to send that signal, but not to an operational system which is sometimes a challenge because you might have to rewrite the code. Deploying models is a really big challenge within Pentaho because it is this fully integrated application. You can deploy the models within Pentaho and not have to jump out into a mainframe environment or something like that. So, I'd say differentiators are very complex data environments, and then this end to end approach where deploying models is much easier than ever before. >> Perhaps, let's talk about alternatives that customers might see. You have a tool suite, and others might have to put together a suite of tools. Maybe tell us some of the geeky version would be the impendent mismatch. You know, like the chasms you'd find between each tool where you have to glue them together, so what are some of those pitfalls? >> One of the challenges is, you have these data scientists working in silos often times. You have data analysts working in silos, you might have data engineers working in silos. One of the big pitfalls is not really collaborating enough to the point where they can do all of this together. So, that's a really big area that we see pitfalls. >> Is it binary not collaborating, or is it that the round trip takes so long that the quality or number of collaborations is so drastically reduced that the output is of lower quality? >> I think it's probably a little bit of both. I think they want to collaborate but one person might sit in Dearborn, Michigan and the other person might sit in Silicon Valley, so there's just a location challenge as well. The other challenge is, some of the data analysts might sit in IT and some of the data scientists might sit in an analytics department somewhere, so it kind of cuts across both location and functional area too. >> So let me ask from the point of view of, you know we've been doing these shows for a number of years and most people have their first data links up and running and their first maybe one or two use cases in production, very sophisticated customers have done more, but what seems to be clear is the highest value coming from those projects isn't to put a BI tool in front of them so much as to do advanced analytics on that data, apply those analytics to inform a decision, whether a person or a machine. >> That's exactly right. >> So, how do you help customers over that hump and what are some other examples that you can share? >> Yeah, so speaking of transformative. I mean, that's what machine learning is all about. It helps companies transform their businesses. We like to talk about that at Pentaho. One customer kind of industry example that I'll share is a company called IMS. IMS is in the business of providing data and analytics to insurance companies so that the insurance companies can price insurance policies based on usage. So, it's a usage model. So, IMS has a technology platform where they put sensors in a car, and then using your mobile phone, can track your driving behavior. Then, your insurance premium that month reflects the driving behavior that you had during that month. In terms of transformative, this is completely upending the insurance industry which has always had a very fixed approach to pricing risk. Now, they understand everything about your behavior. You know, are you turning too fast? Are you breaking too fast, and they're taking it further than that too. They're able to now do kind of a retroactive look at an accident. So, after an accident, they can go back and kind of decompose what happened in the accident and determine whether or not it was your fault or was in fact the ice on the street. So, transformative? I mean, this is just changing things in a really big way. >> I want to get your thoughts on this. I'm just looking at some of the research. You know, we always have the good data but there's also other data out there. In your news, 92% of organizations plan to deploy more predictive analytics, however 50% of organizations have difficulty integrating predictive analytics into their information architecture, which is where the research is shown. So my question to you is, there's a huge gap between the technology landscapes of front end BI tools and then complex data integration tools. That seems to be the sweet spot where the value's created. So, you have the demand and then front end BI's kind of sexy and cool. Wow, I could power my business, but the complexity is really hard in the backend. Who's accessing it? What's the data sources? What's the governance? All these things are complicated, so how do you guys reconcile the front end BI tools and the backend complexity integrations? >> Our story from the beginning has always been this one integrated platform, both for complex data integration challenges together with visualizations, and that's very similar to what this announcement is all about for the data science market. We're very much in line with that. >> So, it's the cart before the horse? Is it like the BI tools are really driven by the data? I mean, it makes sense that the data has to be key. Front end BI could be easy if you have one data set. >> It's funny you say that. I presented at the Gardner conference last week and my topic was, this just in: it's not about analytics. Kind of in jest, but it drove a really big crowd. So, it's about the data right? It's about solving the data problem before you solve the analytics problem whether it's a simple visualization or it's a complex fraud machine learning problem. It's about solving the data problem first. To that quote, I think one of the things that they were referencing was the challenging information architectures into which companies are trying to deploy models and so part of that is when you build a machine learning model, you use R and Python and all these other ones we're familiar with. In order to deploy that into a mainframe environment, someone has to then recode it in C++ or COBOL or something else. That can take a really long time. With our integrated approach, once you've done the feature engineering and the data preparation using our drag and drop environment, what's really interesting is that you're like 90% of the way there in terms of making that model production ready. So, you don't have to go back and change all that code, it's already there because you used it in Pentaho. >> So obviously for those two technologies groups I just mentioned, I think you had a good story there, but it creates problems. You've got product gaps, you've got organizational gaps, you have process gaps between the two. Are you guys going to solve that, or are you currently solving that today? There's a lot of little questions in there, but that seems to be the disconnect. You know, I can do this, I can do that, do I do them together? >> I mean, sticking to my story of one integrated approach to being able to do the entire data science workflow, from beginning to end and that's where we've really excelled. To the extent that more and more data engineers and data analysts and data scientists can get on this one platform even if their using R and WECCA and Python. >> You guys want to close those gaps down, that's what you guys are doing, right? >> We want to make the process more collaborative and more efficient. >> So Dave Alonte has a question on CrowdChat for you. Dave Alonte was in the snowstorm in Boston. Dave, good to see you, hope you're doing well shoveling out the driveway. Thanks for coming in digitally. His question is HDS has been known for mainframes and storage, but Hitachi is an industrial giant. How is Pentaho leveraging Hitatchi's IoT chops? >> Great question, thanks for asking. Hitatchi acquired Pentaho about two years ago, this is before my time. I've been with Pentaho about ten months ago. One of the reasons that they acquired Pentaho is because a platform that they've announced which is called Lumata which is their IoT platform, so what Pentaho is, is the analytics engine that drives that IoT platform Lumata. So, Lumata is about solving more of the hardware sensor, bringing data from the edge into being able to do the analytics. So, it's an incredibly great partnership between Lumata and Pentaho. >> Makes an eternal customer too. >> It's a 90 billion dollar conglomerate so yeah, the acquisition's been great and we're still very much an independent company going to market on our own, but we now have a much larger channel through Hitatchi's reps around the world. >> You've got IoT's use case right there in front of you. >> Exactly. >> But you are leveraging it big time, that's what you're saying? >> Oh yeah, absolutely. We're a very big part of their IoT strategy. It's the analytics. Both of the examples that I shared with you are in fact IoT, not by design but it's because there's a lot of demand. >> You guys seeing a lot of IoT right now? >> Oh yeah. We're seeing a lot of companies coming to us who have just hired a director or vice president of IoT to go out and figure out the IoT strategy. A lot of these are manufacturing companies or coming from industries that are inefficient. >> Digitizing the business model. >> So to the other point about Hitachi that I'll make, is that as it relates to data science, a 90 billion dollar manufacturing and otherwise giant, we have a very deep bench of phD data scientists that we can go to when there's very complex data science problems to solve at customer sight. So, if a customer's struggling with some of the basic how do I get up and running doing machine learning, we can bring our bench of data scientist at Hitatchi to bear in those engagements, and that's a really big differentiator for us. >> Just to be clear and one last point, you've talked about you handle the entire life cycle of modeling from acquiring the data and prepping it all the way through to building a model, deploying it, and updating it which is a continuous process. I think as we've talked about before, data scientists or just the DEV ops community has had trouble operationalizing the end of the model life cycle where you deploy it and update it. Tell us how Pentaho helps with that. >> Yeah, it's a really big problem and it's a very simple solution inside of Pentaho. It's basically a step inside of Pentaho. So, in the case of fraud let's say for example, a prediction might say fraud, not fraud, fraud, not fraud, whatever it is. We can then bring that kind of full lifecycle back into the data workflow at the beginning. It's a simple drag and drop step inside of Pentaho to say which were right and which were wrong and feed that back into the next prediction. We could also take it one step further where there has to be a manual part of this too where it goes to the customer service center, they investigate and they say yes fraud, no fraud, and then that then gets funneled back into the next prediction. So yeah, it's a big challenge and it's something that's relatively easy for us to do just as part of the data science workflow inside of Pentaho. >> Well Arick, thanks for coming on The Cube. We really appreciate it, good luck with the rest of the week here. >> Yeah, very exciting. Thank you for having me. >> You're watching The Cube here live in Silicon Valley covering Strata Hadoop, and of course our Big Data SV event, we also have a companion event called Big Data NYC. We program with O'Reilley Strata Hadoop, and of course have been covering Hadoop really since it's been founded. This is The Cube, I'm John Furrier. George Gilbert. We'll be back with more live coverage today for the next three days here inside The Cube after this short break.